A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network
1 Electric Power Research Institute, Guangdong Power Grid Corporation, 510640, Guangzhou, China
2 School of Electric Power, South China University of Technology, 510640, Guangzhou, China
3 Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, 511458, Guangzhou, China
4 Guangdong Intework Energy Technology Co., Ltd, 511458, Guangzhou, China
Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.
© The Authors, published by EDP Sciences, 2016
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